Data loss and missing values is a common problem in data analysis. Therefore, it is important that by estimating missing values, the data was completed and placed in the proper path. Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse-probability weighting (IPW). In this study, a third approach which is a combination of MI and IPW will be introduced. It can be said by results of the simulation study that IPW/MI can have advantages over alternatives. Regarding the missing values in most studies, especially in the medical field, ignoring them leads to wrong analysis. So, using of robust methods to proper analysis of missing values is essential.

Researchers often face the problem of how to address missing data. Multiple imputation by chained equations is one of the most common methods for imputation. In theory, any imputation model can be used to predict the missing values. However, if the predictive models are incorrect, it can lead to biased estimates and invalid inferences. One of the latest solutions for dealing with missing data is machine learning methods and the SuperMICE method. In this paper, We present a set of simulations indicating that this approach produces final parameter estimates with lower bias and better coverage than other commonly used imputation methods. Also, implementing some machine learning methods and an ensemble algorithm, SuperMICE, on the data of the Industrial establishment survey is discussed,  in which the imputation of different variables in the data co-occurs. Also, the evaluation of various methods is discussed, and the method that has better performance than the other methods is introduced.